2016/11/17 by Subhajit Dutta, Dutta, Subhajit, Anil K. Ghosh +1
Computer Science · Mathematics · #Advanced Statistical Methods and Models #FOS: Computer and information sciences #Face and Expression Recognition #Fuzzy Systems and Optimization #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.1611.05668
openalex publication_date 2016/11/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this article, we use Lp depth for classification of multivariate data, where the value of p is chosen adaptively using observations from the training sample. While many depth based classifiers are constructed assuming elliptic symmetry of the underlying distributions, our proposed Lp depth classifiers cater to a larger class of distributions. We establish Bayes risk consistency of these proposed classifiers under appropriate regularity conditions. Several simulated and benchmark data sets are analyzed to compare their finite sample performance with some existing parametric and nonparametric classifiers including those based on other notions of data depth.